Papers by Faadil Abdullah Shaikh
Statement-Tuning Enables Efficient Cross-lingual Generalization in Encoder-only Models (2025.findings-acl)
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Ahmed Elshabrawy, Thanh-Nhi Nguyen, Yeeun Kang, Lihan Feng, Annant Jain, Faadil Abdullah Shaikh, Jonibek Mansurov, Mohamed Fazli Mohamed Imam, Jesus-German Ortiz-Barajas, Rendi Chevi, Alham Fikri Aji
| Challenge: | Large Language Models excel in zero-shot and few-shot tasks, but their architecture makes them difficult to use. |
| Approach: | They adapt Large Language Models (LLMs) for zero-shot generalization using Statement Tuning . they find encoders can achieve zero- shot cross-lingual generalization . |
| Outcome: | The proposed model generalizes well across languages while being more efficient. |